{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H2O.ai GPU Edition Machine Learning $-$ Multi-GPU GLM Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this demo, we will train 4000 regularized linear regression models (aka Generalized Linear Models or GLMs) on the U.S. Census dataset, with the goal to predict the earned income of a person, given approximately 10000 features such as gender, age, occupation, zip code, etc.\n",
    "\n",
    "### The dataset is about 2GB in memory (50k rows, 10k cols, single-precision floating-point values), so it easily fits onto the GPU memory.\n",
    "\n",
    "### By using multiple GPUs, we are able to speed up this process significantly, and can train about 40 models per second (on a DGX-1 with 8 GPUs) vs 1 model per second on dual-Xeon server."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First time only: Install dependencies\n",
    "#!pip install https://s3.amazonaws.com/h2o-beta-release/goai/h2o4gpu-0.0.2-py2.py3-none-any.whl\n",
    "#!pip install Cython pandas seaborn psutil feather_format\n",
    "#!pip install -e \"git+https://github.com/fbcotter/py3nvml#egg=py3nvml\"\n",
    "\n",
    "## Now restart the kernel to get py3nvml to work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f\n",
    "%matplotlib inline\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data Frame and create raw X and y arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to read data via feather: 1.1981303691864014\n"
     ]
    }
   ],
   "source": [
    "import os, sys, time\n",
    "import feather\n",
    "\n",
    "file = os.path.join(os.getcwd(), \"ipums.feather\")\n",
    "if not os.path.exists(file):\n",
    "   !wget https://s3.amazonaws.com/h2o-public-test-data/h2o4gpu/open_data/ipums.feather\n",
    "t0 = time.time()\n",
    "df = feather.read_dataframe(file)\n",
    "t1 = time.time()\n",
    "print(\"Time to read data via feather: %r\" % (t1-t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We predict the last column \"INCEARN\" - Income earned\n",
    "target = df.columns[-1]\n",
    "cols = [c for c in df.columns if c != target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69e8ec22b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TszAiroiIRyLiyYi4JSKOiYh5Y9QcEBHDEbEqIlZHxI8i4pBxjnNIRFxf2q8q9QeM0X5e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\nJhYAAMAmFgAAwBYU0KHfmKckdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69e5743eb8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 395
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[target].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/navdeep/h2oai/env/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55776, 9732)\n",
      "(55776,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.array(df.ix[:,cols], order='f').astype('float32')\n",
    "y = np.array(df[target].values, dtype='float32')\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the dataset into Training (80%) and Validation (20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fortran order=1\n",
      "Original data rows=55776 cols=9732\n",
      "Training  rows=44621\n",
      "Vaidation rows=11155\n",
      "New data rows=44621 cols=9733\n"
     ]
    }
   ],
   "source": [
    "intercept = True\n",
    "validFraction=0.2\n",
    "standardize = 0\n",
    "lambda_min_ratio = 1e-7\n",
    "\n",
    "if standardize:\n",
    "    print (\"implement standardization transformer\")\n",
    "    exit()\n",
    "\n",
    "# Setup Train/validation Set Split\n",
    "morig = X.shape[0]\n",
    "norig = X.shape[1]\n",
    "fortran = X.flags.f_contiguous\n",
    "print(\"fortran order=%d\" % (fortran))\n",
    "print(\"Original data rows=%d cols=%d\" % (morig,norig))\n",
    "\n",
    "# Do train/valid split\n",
    "HO=int(validFraction*morig)\n",
    "H=morig-HO\n",
    "print(\"Training  rows=%d\" % (H))\n",
    "print(\"Vaidation rows=%d\" % (HO))\n",
    "trainX = np.copy(X[0:H,:])\n",
    "trainY = np.copy(y[0:H])\n",
    "validX = np.copy(X[H:morig,:])\n",
    "validY = np.copy(y[H:morig])\n",
    "trainW = np.copy(trainY)*0.0 + 1.0 # constant unity weight\n",
    "\n",
    "mTrain = trainX.shape[0]\n",
    "mvalid = validX.shape[0]\n",
    "\n",
    "# if using upload_data and fit_ptr, then have to create own intercept as last column in trainX and validX\n",
    "if intercept:\n",
    "    trainX = np.hstack([trainX, np.ones((trainX.shape[0],1),dtype=trainX.dtype)])\n",
    "    validX = np.hstack([validX, np.ones((validX.shape[0],1),dtype=validX.dtype)])\n",
    "    n = trainX.shape[1]\n",
    "print(\"New data rows=%d cols=%d\" % (mTrain,n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define some helper methods for plotting and running the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "def new_alpha(row_fold):\n",
    "    if row_fold == 0:\n",
    "        return -0.025\n",
    "    elif row_fold == 1:\n",
    "        return -0.05\n",
    "    elif row_fold == 3:\n",
    "        return 0.025\n",
    "    elif row_fold == 4:\n",
    "        return 0.05\n",
    "    else: return 0\n",
    "\n",
    "def plot_cpu_perf(axis, cpu_labels, cpu_snapshot):\n",
    "    axis.cla()\n",
    "    axis.grid(False)\n",
    "    axis.set_ylim([0,100])\n",
    "    axis.set_ylabel('Percent', labelpad=2, fontsize = 14)\n",
    "    axis.bar(cpu_labels, cpu_snapshot, color='dodgerblue', edgecolor='none')\n",
    "    axis.set_title('CPU Utilization', fontsize = 16)\n",
    "    \n",
    "def plot_gpu_perf(axis, gpu_labels, gpu_snapshot):\n",
    "    axis.cla()\n",
    "    axis.grid(False)\n",
    "    axis.set_ylim([0,100])\n",
    "    axis.set_xticks(gpu_labels)\n",
    "    axis.set_ylabel('Percent', labelpad=2, fontsize = 14)\n",
    "    axis.bar(gpu_labels, gpu_snapshot, width =0.5, color = 'limegreen',align='center', edgecolor='none')\n",
    "    axis.set_title('GPU Utilization', fontsize = 16)\n",
    "    \n",
    "def plot_glm_results(axis, results, best_rmse, cb):\n",
    "    axis.cla()\n",
    "    axis.set_xscale('log')\n",
    "    axis.set_xlim([1e2, 1e9])\n",
    "    axis.set_ylim([-0.12, 1.12])\n",
    "    axis.set_yticks([x/7. for x in range(0,8)])\n",
    "    axis.set_ylabel('Parameter 1:  '+r'$\\alpha$', fontsize = 16)\n",
    "    axis.set_xlabel('Parameter 2:  '+r'$\\lambda$', fontsize = 16)\n",
    "    num_models = min(4000,int(4000*results.shape[0]/2570))\n",
    "    axis.set_title('Elastic Net Models Trained and Evaluated: ' + str(num_models), fontsize = 16)\n",
    "\n",
    "    try:\n",
    "        from matplotlib.colors import ListedColormap\n",
    "        cm = ListedColormap(sns.color_palette(\"RdYlGn\", 10).as_hex())\n",
    "        cf = axis.scatter(results['lambda'], results['alpha_prime'], c=results['rel_acc'], \n",
    "                    cmap=cm, vmin=0, vmax=1, s=60, lw=0)\n",
    "        axis.plot(best_rmse['lambda'],best_rmse['alpha_prime'], 'o',\n",
    "            ms=15, mec='k', mfc='none', mew=2)\n",
    "\n",
    "        if not cb:\n",
    "            cb = pl.colorbar(cf, ax=axis)\n",
    "            cb.set_label('Relative  Validation  Accuracy', rotation=270, \n",
    "                         labelpad=18, fontsize = 16)   \n",
    "        cb.update_normal(cf)\n",
    "    except:\n",
    "        #print(\"plot_glm_results exception -- no frame\")\n",
    "        pass\n",
    "    \n",
    "def RunAnimation(arg):\n",
    "    import os, sys, time\n",
    "    import subprocess\n",
    "    import psutil\n",
    "    import pylab as pl\n",
    "    from IPython import display\n",
    "    import matplotlib.gridspec as gridspec\n",
    "    import seaborn as sns\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    \n",
    "\n",
    "    print(\"RunAnimation\")\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    deviceCount = arg\n",
    "    # Need this only for animation of GPU usage to be consistent with \n",
    "    #from py3nvml.py3nvml import *\n",
    "    import py3nvml\n",
    "    maxNGPUS = int(subprocess.check_output(\"nvidia-smi -L | wc -l\", shell=True))\n",
    "    print(\"\\nNumber of GPUS:\", maxNGPUS)\n",
    "\n",
    "    py3nvml.py3nvml.nvmlInit()\n",
    "    total_deviceCount = py3nvml.py3nvml.nvmlDeviceGetCount()\n",
    "    if deviceCount == -1:\n",
    "        deviceCount = total_deviceCount\n",
    "    #for i in range(deviceCount):\n",
    "    #    handle = nvmlDeviceGetHandleByIndex(i)\n",
    "    #    print(\"Device {}: {}\".format(i, nvmlDeviceGetName(handle)))\n",
    "    #print (\"Driver Version:\", nvmlSystemGetDriverVersion())\n",
    "    print(\"Animation deviceCount=%d\" % (deviceCount))\n",
    "    \n",
    "    file = os.getcwd() + \"/error.txt\"\n",
    "    print(\"opening %s\" % (file))\n",
    "    fig = pl.figure(figsize = (9,9))\n",
    "    pl.rcParams['xtick.labelsize'] = 14\n",
    "    pl.rcParams['ytick.labelsize'] = 14\n",
    "    gs = gridspec.GridSpec(3, 2, wspace=0.3, hspace=0.4)\n",
    "    ax1 = pl.subplot(gs[0,-2])\n",
    "    ax2 = pl.subplot(gs[0,1])\n",
    "    ax3 = pl.subplot(gs[1:,:])\n",
    "    fig.suptitle('H2O.ai Machine Learning $-$ Generalized Linear Modeling', size=18)\n",
    "\n",
    "    pl.gcf().subplots_adjust(bottom=0.2)\n",
    "\n",
    "    #cb = False\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    cm = ListedColormap(sns.color_palette(\"RdYlGn\", 10).as_hex())\n",
    "    cc = ax3.scatter([0.001, 0.001], [0,0], c =[0,1], cmap = cm)\n",
    "    cb = pl.colorbar(cc, ax=ax3)\n",
    "    os.system(\"mkdir -p images\")\n",
    "    i=0\n",
    "    while(True):\n",
    "        #try:\n",
    "            #print(\"In try i=%d\" % i)\n",
    "            #sys.stdout.flush()\n",
    "            \n",
    "            #cpu\n",
    "            snapshot = psutil.cpu_percent(percpu=True)\n",
    "            cpu_labels = range(1,len(snapshot)+1)\n",
    "            plot_cpu_perf(ax1, cpu_labels, snapshot)\n",
    "    \n",
    "            #gpu\n",
    "            gpu_snapshot = []\n",
    "            gpu_labels = list(range(1,deviceCount+1))\n",
    "            import py3nvml\n",
    "            for j in range(deviceCount):\n",
    "                handle = py3nvml.py3nvml.nvmlDeviceGetHandleByIndex(j)\n",
    "                util = py3nvml.py3nvml.nvmlDeviceGetUtilizationRates(handle)\n",
    "                gpu_snapshot.append(util.gpu)\n",
    "            gpu_snapshot = gpu_snapshot   \n",
    "            plot_gpu_perf(ax2, gpu_labels, gpu_snapshot)\n",
    "    \n",
    "            res = pd.read_csv(file, sep=\"\\s+\",header=None,names=['time','pass','fold','a','i','alpha','lambda','trainrmse','ivalidrmse','validrmse'])\n",
    "            \n",
    "            res['rel_acc'] = ((42665- res['validrmse'])/(42665-31000))\n",
    "            res['alpha_prime'] = res['alpha'] + res['fold'].apply(lambda x: new_alpha(x))\n",
    "\n",
    "            best = res.ix[res['rel_acc']==np.max(res['rel_acc']),:]\n",
    "            plot_glm_results(ax3, res, best.tail(1), cb)\n",
    "            # flag for colorbar to avoid redrawing\n",
    "            #cb = True\n",
    "\n",
    "            # Add footnotes\n",
    "            footnote_text = \"*U.S. Census dataset (predict Income): 45k rows, 10k cols\\nParameters: 5-fold cross-validation, \" + r'$\\alpha = \\{\\frac{i}{7},i=0\\ldots7\\}$' + \", \"\\\n",
    "   'full $\\lambda$-' + \"search\"\n",
    "            #pl.figtext(.05, -.04, footnote_text, fontsize = 14,)\n",
    "            pl.annotate(footnote_text, (0,0), (-30, -50), fontsize = 12,\n",
    "                        xycoords='axes fraction', textcoords='offset points', va='top')\n",
    "\n",
    "            #update the graphics\n",
    "            display.display(pl.gcf())\n",
    "            display.clear_output(wait=True)\n",
    "            time.sleep(0.01)\n",
    "\n",
    "            #save the images\n",
    "            saveimage=0\n",
    "            if saveimage:\n",
    "                file_name = './images/glm_run_%04d.png' % (i,)\n",
    "                pl.savefig(file_name, dpi=200)\n",
    "            i=i+1\n",
    "        \n",
    "        #except KeyboardInterrupt:\n",
    "        #    break\n",
    "        #except:\n",
    "        #    #print(\"Could not Create Frame\")\n",
    "        #    pass\n",
    "        \n",
    "def RunH2Oaiglm(arg):\n",
    "    import h2o4gpu as h2o4gpu\n",
    "\n",
    "    intercept, lambda_min_ratio, nFolds, n_alphas, n_lambdas, n_gpus = arg\n",
    "    \n",
    "    print(\"Begin Setting up Solver\")\n",
    "    os.system(\"rm -f error.txt ; touch error.txt ; rm -f varimp.txt ; touch varimp.txt\") ## for visualization\n",
    "    enet = h2o4gpu.ElasticNetH2O(n_gpus=n_gpus, fit_intercept=intercept, lambda_min_ratio=lambda_min_ratio, n_lambdas=n_lambdas, n_folds=n_folds, n_alphas=n_alphas)\n",
    "    print(\"End Setting up Solver\")\n",
    "\n",
    "    ## First, get backend pointers\n",
    "    sourceDev=0\n",
    "    t0 = time.time()\n",
    "    a,b,c,d,e = enet.prepare_and_upload_data(trainX, trainY, validX, validY, trainW, source_dev=sourceDev)\n",
    "    t1 = time.time()\n",
    "    print(\"Time to ingest data: %r\" % (t1-t0))\n",
    "\n",
    "    ## Solve\n",
    "    if 1==1:\n",
    "        print(\"Solving\")\n",
    "        t0 = time.time()\n",
    "        order='c' if fortran else 'r'\n",
    "        double_precision=0 # Not used\n",
    "        store_full_path=0\n",
    "        enet.fit_ptr(mTrain, n, mvalid, double_precision, order, a, b, c, d, e, source_dev=sourceDev)\n",
    "        t1 = time.time()\n",
    "        print(\"Done Solving\")\n",
    "        print(\"Time to train H2O AI GLM: %r\" % (t1-t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train 4000 Elastic Net Models (5-fold cross-validation, 8 $\\alpha$ values, 100 $\\lambda$ values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import concurrent.futures\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "\n",
    "lambda_min_ratio=1E-9\n",
    "n_folds=5\n",
    "n_alphas=8\n",
    "n_lambdas=100\n",
    "n_gpus=-1 # -1 means use all GPUs\n",
    "\n",
    "arg = intercept, lambda_min_ratio, n_folds, n_alphas, n_lambdas, n_gpus\n",
    "from threading import Thread\n",
    "background_thread = Thread(target=RunH2Oaiglm, args=(arg,))\n",
    "background_thread.start()\n",
    "#futures = []\n",
    "#Executor = ProcessPoolExecutor(max_workers=1)\n",
    "#futures.append(Executor.submit(RunH2Oaiglm, arg)) ## run in separate process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HD+Pbb7/NU23XwsIC48aNw8aNG4V9/v333wgNDdXYsWmSto6rKM7vlJQUODk5Yf/+/ejS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ZreBI60ZcuWTL83TJ599lnVqFEjzWvt27fXV199peTkZK1du1bDhw/PsDfqhg0bjBvpFi1aWPx/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\nnN3v/sBQ1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+qZZ55Rbm5u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\nQgghhBBCCCEd2P8DEatK0Dx85XgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a3ce15ba8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 579
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RunAnimation(n_gpus)\n",
    "#concurrent.futures.wait(futures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
